HSP47: a potential target for fibrotic diseases and implications for therapy
Bibliographic record
Abstract
Introduction: Chronic fibrotic disorders are challenging clinical problems. The major challenge is the identification of specific targets expressed selectively in fibrotic tissues. Collagen accumulation is the hallmark fibrosis. HSP47 is a collagen-specific chaperon with critical role in collagen folding. This review discusses the anti-fibrotic potential of HSP47.Areas covered: This review compiles data retrieved from the PubMed database with keywords ‘HSP47+fibrosis’ from 01/2005 to 06/2020. We examined 1) collagen biology and its role in fibrotic diseases, 2) HSP47 role in fibrosis, 3) HSP47 inhibition strategies and 4) clinical investigations.The identification of the HSP47-collagen binding site led to the development of methods to screen HSP47 inhibitors with anti-fibrotic potential. Specificin vivo delivery systems of HSP47 siRNA to fibrotic tissue reduced collagen production/secretion associated with fibrosis inhibition in preclinical models. This strategy is about to be tested in clinical trials.Expert opinion: As a collagen-specific chaperon, HSP47 is a promising therapeutic target in fibrosis. Preclinical models have shown encouraging anti-fibrotic results. Anti-HSP47 strategies need to be further evaluated in clinical trials. The increase in circulating-HSP47 in lung fibrosis patients highlights the potential of HSP47 as a noninvasive biomarker and may represent an important step toward personalized medicine in fibrotic disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".